SBIR Phase I: Novel Spatial Speech Separation Techniques to Improve Speaker Identification and Speech Recognition

这个小企业创新研究（SBIR）第一阶段的研究项目侧重于一种使用多个麦克风来空间分离语音的新方法，从而提高说话者识别和语音识别的准确性。该项目的第一个组成部分将应用特征驱动的自适应波束形成，以提高嘈杂和混响环境下的语音识别精度。与传统的波束形成方法不同，该方法基于特征提取和识别模块的反馈进行权重自适应。该阵列可以消除方向干扰，减少混响效应。对于该项目的第二部分，将使用一种新的连续变量掩码估计方法来估计分离语音的哪些部分包含具有良好信噪比（SNR）的语音，并应用语音重建来修复这些区域。修复后的语音将被输入语音识别/说话人识别引擎。第三，该项目将应用由人类双耳感知激发的想法。强大的说话人识别和语音识别的潜在市场预计将是巨大的，范围从嘈杂办公室的语音听写，金融交易的声纹验证，生物识别，自动语音响应系统中的语音识别，移动设备的语音搜索等等。此外，在军事指挥、控制和通信领域也有许多军事应用，在这些领域，向其他接收者传达正确的命令是很重要的。

This Small Business Innovation Research (SBIR) Phase I research project focuses on a novel approach that uses multiple microphones to spatially separate speech and hence enhance speaker identification and speech recognition accuracy. The first component of the project will apply feature-driven adaptive beam forming to improve speech recognition accuracy in noisy and reverberant environments. Unlike conventional beam forming methods, this proposed method performs weight adaptation based on feedback from the feature extraction and recognition modules. The arrays can eliminate directional interferences and reduce reverberant effects. For the second component of the proposed project, a new continuously-variable mask estimation method will be used to estimate which parts of the separated speech contain speech with good signal-to noise ratio (SNR), applying speech reconstruction to repair these regions. The repaired speech will then be fed into the speech recognition/speaker identification engine. Thirdly, the project will apply ideas motivated by human binaural perception.The potential market for robust speaker identification and speech recognition is projected to be large, ranging from voice dictation in noisy offices, voiceprint verification for financial transactions, biometrics, speech recognition in automated voice response systems, voice-based search for mobile devices, and several others. Additionally there are many military applications in military command, control, and communications segments, where it is important to convey the correct commands to the other recipients.